using System;

using System.Linq;

using System.Collections.Generic;

​

public class Program

{

    public static void Main()

    {

        // Randomizer

        var r = new Random();

        // Number of classifications

        const int numberOfItems = 100;

        const int classifications = 5;

        // Define the prices of each code

        var prices = new Dictionary<int, int>(){

            { 1, 1000 },

            { 2, 2000 },

            { 3, 3000 },

            { 4, 4000 },

            { 5, 5000 }

        };

        // Data set

        var data = Enumerable.Range(1, numberOfItems).Select(x => (int)r.Next(1, classifications + 1));

        // Build the recommendation set

        var recommendationSet = GenerateRecommendationSetFromDataWithNormalDistribution(data, classifications);

        // Profit/loss

        var net = 0;

        // Generate recommendations based on tests

        foreach (var g in recommendationSet.Groups.OrderBy(g => g.Section))

        {

            var netAdjustment = g.Normalized - g.Original;

            // Adjust net total

            net += (prices[g.Section] * netAdjustment);

            // Format net adjustment

            var netAdjustmentFormatted = (netAdjustment > 0) ? $"+{netAdjustment}" : $"{netAdjustment}";

            // Calculate the number of elements within this set

            Console.WriteLine($"CODE {g.Section}: {g.Original} -> {g.Normalized} ({netAdjustmentFormatted})");

        }   

        Console.WriteLine();

        Console.WriteLine($"NET WITH ADJUSTMENTS: {net:C}");

    }

    public class Test

    {

        public int Code { get; set; }

    }

    public static (IEnumerable<double>, double, double) GenerateComparisonSetForSize(int size)

    {

        var r = new Random();

        // Store things to return

        var list = new List<double>();

        var min = double.MaxValue;

        var max = double.MinValue;

        for (var i = 0; i < size; i++)

        {

            var n = r.NextGaussian();

            if (n > max) { max = n; }   

            if (n < min) { min = n; }

            list.Add(n);

        }

        return (list, min, max);

    }

    public static RecommendationSet GenerateRecommendationSetFromDataWithNormalDistribution(IEnumerable<int> data, int buckets)

    {

        // Build grouped distribution based on number of elements

        (IEnumerable<double> nd, double min, double max) = GenerateComparisonSetForSize(data.Count());

        var recommendationSet = new RecommendationSet(){

            Total = data.Count()    

        };

        // Build the recommendation groups

        var list = new List<RecommendationGroup>();

        // Calculate interval

        var comparisonInterval = (max - min) / buckets;

        for(var i = 1; i <= buckets; i++)

        {

            var cMin = (min + ((i - 1) * comparisonInterval));

            var cMax = cMin + comparisonInterval;

            // Check between the start interval and the end of it

            list.Add(new RecommendationGroup(){

                Section = i,

                Original = data.Count(x => x == i),

                Normalized = nd.Count(x => x >= cMin && x <= cMax)

            });

        }

        recommendationSet.Groups = list;

        return recommendationSet;

    }

}

​

public class RecommendationSet

{

        public IEnumerable<RecommendationGroup> Groups { get; set;}

        public int Total { get; set; }

}

public class RecommendationGroup

{

        public int Section { get; set; }

        public int Original { get; set; }

        public int Normalized { get; set; }

}

​

public static class RandomExtensions

{

        public static double NextGaussian(this Random r, double mu = 0, double sigma = 1)

        {

            var u1 = r.NextDouble();

            var u2 = r.NextDouble();

​

            var rand_std_normal = Math.Sqrt(-2.0 * Math.Log(u1)) * Math.Sin(2.0 * Math.PI * u2);

​

            var rand_normal = mu + sigma * rand_std_normal;

​

            return rand_normal;

        }

}

​